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Faculty of Mathematics

 

This is a list of academic projects available for summer 2017. More projects will be added as they become available.


Value of Information in Early Phase Clinical Trials



Contact Name:

Simon Bond

Contact email:

simon.bond@addenbrookes.nhs.uk

Lab/Department:

Cambridge Clinical Trials Unit

Contact Address:

Addenbrooke's Hospital, Coton House Level 6 - Box 401

Hills Road

Period of the Project:

summer 2017

Brief Description of  Project:

Exploring and developing novel tools to design clinical trials and justify the choice of sample size.

 

A current standard method for choosing a sample size in confirmatory clinical trials is based around hypothesis testing and providing enough statistical power. This does not generalise at all well to exploratory studies in early clinical research. An alternative method called “Value of Information”, is rooted in decision theory and health economic analysis, and attempts to quantify the financial value of gaining information. Methods have been developed to parallel the traditional hypothesis-test-statistical-power approach of late stage confirmatory studies, but little work exists on early stage studies; particularly when there is uncertainty about the standard deviation of an endpoint, on top of uncertainty around its mean value.

Skills Required:

Statistical inference, computer programming

Skills Desired:

familiarity with R programming language

clinical trials

decision theory

economics

Project Open to:

Part III Students

Deadline to register interest:

 

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Mathematical Solutions for Aeronautical Gas-turbine Noise Emissions

Contact Name: Luca Magri
Contact email: lm547@cam.ac.uk
Lab/Department: Engineering Department
Contact Address: Engineering Department
JDB, Fluid Dynamics group
Trumpington Street
Cambridge CB2 1PZ
Period of the Project: June - Sept 2017
Brief Description of Project: Combustion noise is one of the dominant causes of noise pollution generated by the whole turbojet, which is bound to increase with the implementation of low-emission aeroengines. Entropy and vorticity inhomogeneities exiting the combustion chamber accelerated through the turbine are the two well-known indirect mechanisms that produce combustion noise. Recently, we discovered a third indirect noise mechanism caused by inhomogeneities in the gas composition. The system of hyperbolic equations was discretized numerically and solved. Numerical solutions are, however, time consuming and depend on the numerical scheme used.

The objective is to find a full analytical solution with mathematical techniques originated from Quantum Mechanics. The level of noise will be calculated for different Mach numbers, nozzle geometries and flow configurations. The analytical solution will overcome the approximate asymptotic methods used in the literature and industry.

Skills Required: Some familiarity with the following topics would be an advantage, but it not essential:

- Fluid dynamics and/or acoustics
- Hyperbolic Partial Differential Equations
- Perturbation methods

Skills Desired:  
Project Open to: Part III (master's) students, PhD Students
Deadline to register interest:  

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Signal Extraction Techniques for 21-cm Cosmology Radiometric Experiments

Contact Name: Eloy de Lera Acedo
Contact email: eloy@mrao.cam.ac.uk
Lab/Department: Physics
Contact Address: Astrophysics Group
Battcock centre for Astrophysics
Cavendish Laboratory
University of Cambridge
JJ Thomson Avenue
Cambridge CB3 0HE
Period of the Project: 8-12 weeks
Brief Description of Project: The Cosmic Dawn Epoch of the Epoch of Re-ionization are early cosmic epochs (until ~ 1 billion years after the Big Bang) when the Universe went from being a vast volume filled with chemical elements such as Hydrogen to become the realm of celestial objects (stars, galaxies, black holes, etc.) we can see today from Earth. The precise physical processes that took place at the time leading to the Universe we know today are unknown and their study is considered to be the last frontier in cosmological science. It is recognized that studying the red-shifted radio frequency emission from Hydrogen itself (the raw material that formed the first luminous objects) one could probe these epochs through cosmological time and shed light on some of the biggest remaining mysteries in the history of the cosmos. Super instruments like the SKA telescope will aim in a few years to do full tomography of these epochs. However, with a single antenna radiometer it is theoretically possible to detect the sky-averaged hyperfine transition line of atomic hydrogen (red-shifted from 21-cm to a few m due to the expansion of the Universe) in a matter of a few days. This however would require an extraordinary knowledge of the radio instrument and its spatial and spectral effects on both the cosmological signal and the much brighter foreground emissions. In this project the student will work on the development of the algorithms and signal extraction techniques required for the detection of the cosmic signal in this type of experiments. The student will work on a software framework and will back his findings with realistic simulations including the instrument model, the foregrounds and the cosmological signal to prepare for the observations and data analysis.
Skills Required: - Programming skills: Python/Matlab
- Knowledge of signal extraction techniques (eg. Matched filter)
- Knowledge of Bayesian theory
Skills Desired: - Background in experimental cosmology
- Strong background in signal extraction techniques (eg. Matched filter)
- Strong background in Bayesian theory
Project Open to: Undergraduates, Part III (master's) students, PhD Students
Deadline to register interest: 3 March

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Compressed Sensing Development for Nuclear Magnetic Resonance (NMR) Spectroscopy

Contact Name: Mark Bostock
Contact email: mjb218@cam.ac.uk
Lab/Department: Biochemistry (Laboratory of Dr. Daniel Nietlispach)
Contact Address: Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Old Addenbrooke's Site, Cambridge CB2 1GA.
Period of the Project: 8 weeks (negotiable)
Brief Description of Project: NMR spectroscopy is a widely used technique in Biology and Chemistry enabling atomic resolution structural analysis of molecules such as proteins, providing insights into their function and dynamic behaviour.

Over recent years we have been actively developing new data processing methodologies that enable the reconstruction of NMR data that is incompletely sampled in the time domain. Generally these methods are termed 'non uniform sampling’ (NUS) and typically these methods require non-Fourier Transform reconstruction techniques to convert irregularly sampled time domain data into the frequency domain. We have been working on the development and implementation of methodologies known as 'compressed sensing’ (CS), based on l_p-norm minimisation, where typically p=1. CS arose in the literature of information theory [1], [2] and has been applied widely for example in MRI [3] and NMR [4], [5] as well as other areas such as image compression, astronomy, tomography etc. [6]. The area is revolutionising the NMR field allowing us to obtain information which was previously inaccessible, and increasing the range of challenging biomolecules and scenarios which NMR can study. The main benefits of the combination of NUS recording and CS data reconstruction are increases in signal-to-noise and spectral resolution, both typically limiting factors in NMR spectroscopy.

Our current interests are the following:
1)Algorithm development
CS is an actively developing area within applied maths. New algorithms are regularly released with improvements in speed and reconstruction accuracy. We are interested in implementing some of these new algorithms for NMR data processing and assessing any improvements over the existing algorithms. This would require a literature search to identify new algorithms and then coding the algorithm within our existing software package for application to NMR data reconstruction.

2)Reducing the sampling requirements with prior information
Prior information is often available in NMR studies from existing experiments. Repeat measurements frequently look at spectral changes (difference experiments) to track biological processes. This prior information should allow a substantial reduction in sampling requirements. This would involve working with, for example, protein dynamics data and developing the existing algorithm to use available prior information.

3)Investigating the optimum sampling requirements
A currently under-explored area of this field is determining the optimum sampling schedule to use for data acquisition. While some general principles are understood [7], the requirements are likely to vary dependent on the properties of different experiments [8]. A good/bad sampling schedule can have a significant impact on reconstruction quality. Consequently a significant part of this research will involve developing metrics to assess the quality of different schedules and identifying optimum schedules for different experiments.

4)Develop GPU (graphical processing unit) approaches
Reconstruction times for CS processing of NMR data benefit significantly from parallelisation. GPUs are a currently under-exploited resource in this area. Adapting the existing code to use GPUs would provide further significant speed-ups in processing time, further expanding the range of experiments that can be studied.

References:
[1]E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information.,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006.
[2]D. L. Donoho, “Compressed sensing.,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
[3]M. Lustig, D. L. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging.,” Magn. Reson. Med., vol. 58, no. 6, pp. 1182–1195, Dec. 2007.
[4]D. J. Holland, M. J. Bostock, L. F. Gladden, and D. Nietlispach, “Fast multidimensional NMR spectroscopy using compressed sensing,” Angew. Chemie Int. Ed., vol. 50, no. 29, pp. 6548–6551, Jun. 2011.
[5]K. Kazimierczuk and V. Y. Orekhov, “Accelerated NMR spectroscopy by using compressed sensing,” Angew. Chemie Int. Ed., vol. 50, no. 24, pp. 5556–5559, Apr. 2011.
[6]D. J. Holland and L. F. Gladden, “Less is More: How Compressed Sensing is Transforming Metrology in Chemistry,” Angew. Chemie Int. Ed., vol. 53, pp. 13330–13340, 2014.
[7]S. G. Hyberts, H. Arthanari, and G. Wagner, “Applications of non-uniform sampling and processing,” Top. Curr. Chem., vol. 316, pp. 125–148, 2012.
[8]K. Kazimierczuk and V. Y. Orekhov, “A comparison of convex and non-convex compressed sensing applied to multidimensional NMR.,” J. Magn. Reson., vol. 223, pp. 1–10, Aug. 2012.


 
Skills Required: Good programming expertise, particularly with Python. Interest in information theory.
Skills Desired:  
Project Open to: Undergraduates, Part III (master's) students, PhD Students
Deadline to register interest: 3 March

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Modelling the light distribution in photoacoustic imaging

Contact Name: Joanna Brunker
Contact email: jb2014@cam.ac.uk
Lab/Department: Department of Physics and CRUK Cambridge Institute
Contact Address: Cancer Research UK Cambridge Institute
University of Cambridge
Li Ka Shing Centre
Robinson Way
Cambridge
CB2 0RE
Period of the Project: 31 July – 22 September
Brief Description of Project: Photoacoustic imaging is an emerging technique involving generation of ultrasound using pulses of laser light. Absorption of light by tissue chromophores such as haemoglobin in blood induces a small temperature rise leading to an increase in pressure, and consequently generation of ultrasound waves. Detection of the ultrasound at the tissue surface enables a map of tissue absorption to be reconstructed, since the amplitude of the ultrasound signal is proportional to the absorption. However, the photoacoustic imaging community faces a significant challenge in using the reconstructed images to accurately quantify the concentration of the absorbers, for example to calculate the concentration of oxyhaemoglobin in blood to find the blood oxygenation. The reason for this is that the absorption is proportional not only to the absorption coefficients of the tissue components, and their concentration, but also to the intensity of light incident on these tissue components (the light fluence). The light fluence distribution can be estimated experimentally, or by using models such as Monte Carlo or the diffusion equation, and then used to correct the photoacoustic images to more accurately quantify the absorber concentrations. We have already successfully implemented a correction using the diffusion equation in 2D, but still need to validate this against other models such as Monte Carlo, and to investigate corrections in 3D. The project will address these challenges using MATLAB modelling of both simulated and experimental data representing tissue absorption in a living mouse.
Skills Required: Proficiency with MATLAB
Skills Desired: Familiarity with image reconstruction and light models
Familiarity with Monte Carlo and related computational algorithms
Project Open to: Undergraduates, Part III (master's) students, PhD Students
Deadline to register interest: 3 March

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Spatiotemporal dynamics of plant growth hormone gibberellin (GA) and cellular growth in plant cells

Contact Name: Alexander Jones
Contact email: alexander.jones@slcu.cam.ac.uk
Lab/Department: Sainsbury Laboratory Cambridge University
Contact Address: Sainsbury Laboratory, Cambridge University
Bateman St.
Cambridge, CB2 1LR
Period of the Project: 8 weeks
Brief Description of Project: A major challenge in plant biology is to understand how multicellular organisms integrate dynamic developmental and environmental inputs to drive cellular responses. These cellular processes are well orchestrated across the spatial and temporal scales to enhance tissue- and organ-level functionality. Understanding the mechanisms that control the cellular processes, such as hormone levels and cellular growth, requires a multidisciplinary and systems-level approach.

The project is focused on using a combined approach of cell biology, molecular genetics, and mathematical modelling to visualize and quantify hormone levels that contribute to plant cellular growth. The plant hormone gibberellin (GA) is a powerful growth regulator that controls key developmental transitions such as germination, flowering and fruiting. The Jones group is using a novel FRET biosensor for GA (GPS1) to reveal GA patterns and dynamics in living plants. As a first step, GA will be visualized (using GPS1 biosensor) in growing cells using confocal microscopy, and a spatiotemporal map of GA levels and cellular growth rates will be developed. To test hypotheses about the dynamic relationship between GA levels and cell growth, GA levels will be manipulated using mutant plants defective in GA biosynthesis and treatments with exogenous GA. The quantitative data generated from these experiments will be used to feedback on ongoing 3D finite element model of growing cells, and the key predictions of the models will be tested through the experimental approaches. We anticipate an iterative combination of modelling and experimental work leading to a holistic understanding of how spatiotemporal patterns of GA growth hormone quantitatively and mechanistically relate to cellular growth.

The student will gain experience in: 1. plant culture methods; 2. cutting-edge confocal microscopy techniques (FRET) to collect 3D time-lapse images of GA levels and cellular growth in growing cells; 3.advanced 4D image processing; 4. develop metrics describing the quantitative relationship between GA levels and cell growth; 5. participate in the ongoing development of a 3D finite element computational model aimed at integrating GA hormone levels, physical properties of cell wall, and cellular growth. The student will work closely with a postdoc (Ankit Walia) to collect and analyze data.

Skills Required: Basic knowledge of biology and programming in e.g. matlab/python/c++ would be an advantage, but there are no strict prerequisites for this project.
Skills Desired: An interest in plant development and microscopy would be a great start!
Project Open to: Undergraduates, Part III (master's) students
Deadline to register interest: 3 March

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Stem cell packing

Contact Name: Lee Hazelwood
Contact email: lee.hazelwood@cruk.cam.ac.uk
Lab/Department: CRUK
Contact Address: Dr Lee Hazelwood
Cancer Research UK Cambridge Institute
University of Cambridge
Li Ka Shing Centre
Robinson Way
Cambridge CB2 0RE
Lee.Hazelwood@cruk.cam.ac.uk
Period of the Project: 8 weeks
Brief Description of Project: Intestinal crypts contain stem cells that maintain the intestine. These stem cells replicate and differentiate into paneth cells in the intestine forming an organised intercalated chessboard like pattern, see Figure 1a in ref [1]. The aim of this project is to develop a Cellular Potts model in order to understand how intercellular interactions lead to the observed stem and paneth cell organisation. We will do this by developing a Cellular potts code based on the original work by Craner and Glazier [2] for a simple geometry 2D geometry.

This project is self contained.

[1] Sato et al. Nature 469, 415–418 (20 January 2011) doi:10.1038/nature09637
[2] Graner, F., Glazier, J.A.: Simulation of biological cell sorting using a two dimensional extended Potts model. Phys. Rev. Lett. 69, 2013–2016 (1992)

Skills Required: Coding in Matlab, C or R.
Previous experience in carrying out some simulations on Grids.
Skills Desired: Physical understanding of energy functions.
Project Open to: Part III (master's) students, PhD Students
Deadline to register interest: 3 March

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Single cell normalisation

Contact Name: Lee Hazelwood
Contact email: lee.hazelwood@cruk.cam.ac.uk
Lab/Department: CRUK
Contact Address: Dr Lee Hazelwood
Cancer Research UK Cambridge Institute
University of Cambridge
Li Ka Shing Centre
Robinson Way
Cambridge CB2 0RE
Lee.Hazelwood@cruk.cam.ac.uk
Period of the Project: 8 weeks
Brief Description of Project: We are beginning to acquire genetic measurements (expression levels of all genes) from the single biological cells that comprise tissues. Early data indicates these genetic measures are heterogeneous across, potentially indicating different developmental programmes for these cells. However, one potential confounding factor to our confidence in these predictions is our ability to measure lowly expressed genes, which is often absent from the data. See for example [1].

This project will look at different methods to normalise this data and see how they are affected by missing data. Possible parts of the project depending on ones background and interest might include
- simulating idealised datasets for normalisation (require general coding)
- normalising real datasets using different methods (requires R experience)
- developing a novel normalisation method (more mathematical)

The project is self contained.

[1] A step-by-step workflow for low level analysis of single cell RNA-seq data with bioconductor, Lun AT, McCarthy, DJ, Marioni JC, F1000Res. 2016 Aug 31;5 2122.

Skills Required: Familiarity with coding in Matlab, C and R.
Simulation of data according to particular distributions.
Experience using convolutions.
Skills Desired: Interest in bioinformatics.
Project Open to: Part III (master's) students, PhD Students
Deadline to register interest: 3 March

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Application of Compressive Sensing to Hyperspectral Endoscopy

Contact Name: Dr. Sarah Bohndiek
Contact email: seb53@cam.ac.uk
Lab/Department: Physics
Contact Address:

Dr. Jonghee Yoon (daily supervisor)
jy385@cam.ac.uk
Department of Physics

Period of the Project: 8 - 10 weeks (negotiable before starting the project)
Brief Description of Project: Endoscopic surveillance is crucial for diagnosing cancer in gastrointestinal tract. Current endoscopic methods measure structural information and some biochemical features of tissue but this information does not provide sufficient contrast for early detection of the disease. In order to overcome this limitation, we are developing hyperspectral endoscopy. Hyperspectral imaging techniques measure the full spatial and spectral characteristics of tissue, which would enable early detection by combining contrast relating to structural, biochemical and molecular characteristics of the tissue. Both scanning (spatial & spectral) and snap-shot methods can be used to obtain hyperspectral images requiring either long operation times or limited spatial / spectral resolution, respectively. In this project, we will exploit compressive sensing (CS) techniques to develop real-time hyperspectral endoscopy while retaining high spatial and spectral resolution. CS is an imaging compression method that can achieve high speed by reducing the number of samples required to reconstruct an image. Recently, CS has been applied to various biomedical techniques including fluorescence microscopy, photoacoustic microscopy, X-ray imaging, and hyperspectral imaging. However, there are many challenges in applying CS to hyperspectral endoscopy. The aim of this project would be to develop new theoretical CS framework for hyperspectral endoscopy. The candidate would require a literature search to identify opportunities for CS and then perform simulations to develop and validate the new algorithm. Time allowing, there will be opportunity to apply the new algorithm to real hyperspectral endoscopic experiments ongoing in the laboratory. The developed CS method would be versatile for transferring hyperspectral endoscopy to clinical level.

References
[1] Studer V, Bobin J, Chahid M, Mousavi HS, Candes E, Dahan M. Compressive fluorescence microscopy for biological and hyperspectral imaging. P Natl Acad Sci USA 2012;109:E1679-E1687
[2] Zhao R, Wang Q, Shen Y, Li J. Multidimensional dictionary learning algorithm for compressive sensing-based hyperspectral imaging. Journal of Electronic Imaging 2016;25:063013-063013

Skills Required: Programming skills (Matlab), but they can be learned on the project
Skills Desired: Familiarity with compressed sensing theory and developing algorithms. Ability to communicate with non-mathematicians
Project Open to: Undergraduates
Deadline to register interest:  

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Tensor Networks on TensorFlow

Contact Name: Austen Lamacraft
Contact email: al200@cam.ac.uk
Lab/Department: Physics
Contact Address: TCM, Department of Physics, Cavendish Laboratory
Period of the Project: Summer 2017
Brief Description of Project: Understanding large quantum systems is central to many areas of science. The exponentially large Hilbert space of the problem is the main impediment to any general calculational method. A recent breakthrough in describing low dimensional systems (normally chains of interacting spins) uses tensor networks, in which the state of the system is represented using a network of matrices of much smaller dimension than that of the full Hilbert space.

The goal of this project is to implement a tensor network scheme using TensorFlow, Google's open source library for computations using data flow graphs. This is an efficient way to represent the tensor network algorithm, and allows performance to be maximized using GPUs.

See https://arxiv.org/abs/1609.01552 for an introduction to the physics of the problem.

Skills Required:  
Skills Desired: Familiarity with Python.
Project Open to: Part III (master's) students, PhD Students
Deadline to register interest: 3 March 2017

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Modelling signal processing for stem cell control

Contact Name: Henrik Jönsson
Contact email: henrik.jonsson@slcu.cam.ac.uk
Lab/Department: Sainsbury Laboratory
Contact Address: Sainsbury Laboratory, Bateman Street, Cambridge CB2 1LR
Period of the Project: 8 weeks
Brief Description of Project: "Continuous organ formation in plants is driven by a cluster of stem cells in the tip of their shoots. A regulatory network decides which cell should maintain its stem cell identity, and which cell should instead specialize and start forming a new organ. At the centre of this regulatory network lies the negative feedback between the protein CLAVATA3 (CLV3) (produced by the stem cells) and the stem cell promoting transcription factor WUSCHEL (WUS), produced in a separate region of cells.

The dynamics of negative feedback is well understood, but things get more complicated as the signalling from CLV3 to WUS goes through several receptors. The receptors all perform signal processing on their own and at least one of them exhibit adaptation, meaning that it effectively filters out the average CLV3 input and only responds to recent changes. How do the other receptors work, and how is the signalling from the three integrated to a single output?

There is published data on what happens when you mutate these receptors, both one by one and in pairs. There is also data for how WUS is affected by changes in CLV3 concentration. With this, and with computational modelling, we aim to infer the relationship between these receptors, and what signal processing they must perform.

This project have some clear goals, and a well defined place to start. But the project is easily extensible, and while we have ideas of where one could go next you would be in charge of your own research. The project is also adaptable from a more analytical to a mainly computational approach.


 
Skills Required: Solving and analysing differential equations.
Skills Desired: Experience with programming/scripting will be useful.
Project Open to: Undergraduates, Part III (master's) students, PhD Students
Deadline to register interest: 3 March 2017

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Modeling the evolutionary dynamics leading to Acute Myeloid Leukemia

Contact Name: Jamie Blundell
Contact email: jrb75@stanford.edu
Lab/Department: Cambridge Cancer Center & Deptartment of Oncology
Contact Address: Cambridge Cancer Center, Early Detection Program
Hutchison MRC Research Center
Cambridge Biomedical Campus
Hills Road
Cambridge CB2 0XZ
Period of the Project: June - Sept (flexible)
Brief Description of Project: Cancer is an evolutionary disease, but our quantitative understanding of its onset and progression remain basic. It is widely accepted that cancer develops by alterations in specific sets of genes. While a huge number of studies over the past decade have identified such alterations, much remains to be discovered about cancer dynamics, especially at a quantitative level. Better treatments will come from a deeper understanding of cancer’s evolutionary dynamics and will provide vital information for early detection and intervention. However, addressing these questions relies on the ability to quantitatively understand the clonal dynamics in both healthy and transformed tissues.

The need for a quantitative understanding of cancer evolution is keenly highlighted by blood malignancies such as Acute Myeloid Leukemia (AML). At diagnosis, most AML patients harbour mutations in a ~3 - 10 out of a possible ~100 or so AML-associated genes. The currently accepted theory is that these mutations are acquired sequentially: clones with more mutations are fitter than those with fewer and selectively outcompete them. However, given the low mutation rates and lack of genomic instabilities in these cancers, exactly how it is possible to accumulate this many mutations in slowly cycling stem cell populations has been difficult to explain. Further complicating the issue, recent deep sequencing studies suggest that even in “healthy” people, there is a large degree of clonal evolution: subclones harboring different sets of somatic mutations compete with one another over the course of your life.

The goal of this research project will be to model how mutations arise and expand in tissues in order to understand what genomic signatures might be indicative of early cancer. Specifically we will: (i) Develop a stochastic “null” model of mutation dynamics in healthy (non-cancerous) tissues, maintained by a hierarchy of cell types. (ii) Modify the above model to include “driver” mutations that disrupt the homeostatic balance of the tissue, to model early cancer onset.

Methods used will be a combination of branching processes, nonlinear dynamics and stochastic simulations.

Skills Required: Some exposure to stochastic processes, probability, asymptotic methods desired. Some programming also desired (Python, Matlab or similar).
Skills Desired:  
Project Open to: Part III (master's) students, PhD Students
Deadline to register interest: 3rd March

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Detection of animal faecal parasites using image analysis with an ioLight portable microscope

Contact Name: Carola-Bibiane Schönlieb
Contact email: cbs31@cam.ac.uk
Lab/Department: DAMTP
Contact Address: DAMTP, Wilberforce Road, CB3 0WA
Period of the Project: 8 weeks
Brief Description of Project: The over use of antibiotics is causing pathogens to become resistant to antibiotics, potentially leaving us without effective treatments for a wide range of conditions. One of the causes of antibiotic resistance is the regular and indiscriminate use of antibiotics in livestock - frequently farmers see a problem in one animal and immediately treat all of the heard or flock with antibiotics as a precaution, without establishing if antibiotics are necessary. The authorities are now starting to limit the use of antibiotics in livestock by imposing strict limits on the levels of antibiotics that the meat can contain. This forces farmers and vets to be much more selective with antibiotics and only use them when really needed. To do this, farmers need to be able to accurately diagnose disease on the farm, rather than sending samples off to a lab and waiting days for a diagnosis.
Intestinal parasites are one of the more common issues for which antibiotics are used in cattle and sheep. There are a large number of parasites and only a small number are harmful, so to determine if antibiotics are required the faeces need to be analysed and the number and type of parasites present measured. This is done using a microscope to look for parasite eggs in the faeces, then counting the different types of egg found. Normally this is done with a bench microscope in a lab, and an experienced technician or parasitologist to manually identify and count the eggs. The new ioLight portable microscope has sufficient resolution to enable this to be done in the field, thus reducing the time required for diagnosis so the farmer immediately knows if treatment with antibiotics is appropriate or not. It is impractical to train farmers to manually count and identify eggs, so an automated image analysis solution is required to analyse the images from the microscope and count eggs. The image analysis software needs to work in the field, so it needs to operate on the relatively low powered Raspberry Pi within the microscope, but still deliver results quickly.

This project is to develop image analysis software running on a Raspberry Pi to analyse microscope images of faecal samples, identify the eggs present, then measure dimensions of the eggs and other simple parameters that could then be used to categorise the eggs. Efficient image analysis algorithms are required to ensure that the results are delivered to the user without significant delay.
To enable this project, ioLight will provide a microscope (containing the Raspberry Pi) and give assistance in learning how to use the microscope and get access to the images on the Raspberry Pi. MatLab, running on the Raspberry Pi could be used to develop the image analysis software.

This project will be jointly supervised by
Joana Grah jg704@cam.ac.uk
Jasmina Lazic Jasmina.Lazic@mathworks.co.uk
Stefanie Reichelt Stefanie.Reichelt@cruk.cam.ac.uk
Carola-Bibiane Schönlieb cbs31@cam.ac.uk
Richard Williams richard.williams@iolight.co.uk

Skills Required: Maths 1a and 1b. Versatile in MATLAB programming. A curious mind and a passion for problem solving.
Skills Desired: Numerical analysis (numerical linear algebra and numerics for differential equations), harmonic analysis, partial differential equations.
Experience in programming on the Raspberry Pi.
Project Open to: Undergraduates, Part III (master's) students
Deadline to register interest: 3 March 2017

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